Maus, Rachel L

Abstract Motivation Multiplex immunofluorescence (MxIF) enables the quantification of multiple protein markers at a single-cell level while preserving spatial information, offering a powerful tool for studying tissue microenvironments. However, the flexibility in MxIF panel design poses challenges in standardizing cell phenotyping. Results We present SpaFlow, an efficient, customizable pipeline for unsupervised clustering and classification of MxIF data, implemented using Nextflow. SpaFlow performs quality control, clustering, and postclustering analysis on segmented and quantified MxIF data, facilitating reproducible and scalable analyses across various computing platforms. The SpaFlow pipeline integrates three clustering and classification packages—Seurat, SCIMAP, and CELESTA—each providing unique methodologies for identifying cell types based on phenotypic markers. A novel “meta-clustering” approach condenses clusters across multiple regions of interest into common meta-clusters, streamlining the cell-type identification process in large datasets. SpaFlow’s robust quality control steps, including signal summation and cell density filtering, mitigate artifacts that may impact clustering accuracy. We demonstrate the utility of SpaFlow in a case study involving 297 ovarian tumor cores, where SpaFlow successfully identified biologically meaningful cell populations, including tumor-infiltrating lymphocytes, efficiently and rapidly. Additionally, SpaFlow’s reproducibility is validated using serial tonsil sections, confirming its capability to consistently identify distinctive cell populations across matched ROIs. Availability and implementation SpaFlow is freely available with detailed documentation and examples at https://github.com/dimi-lab/SpaFlow.

Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4′,6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining–scanning–bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining–scanning–bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts’ annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.

AbstractThrough our involvement in KEYNOTE‐059, we unexpectedly observed durable responses in two patients with metastatic gastroesophageal adenocarcinoma (mGEA) who received ramucirumab (anti‐VEGFR‐2)/paclitaxel after immune checkpoint inhibition (ICI). To assess the reproducibility of this observation, we piloted an approach to administer ramucirumab/paclitaxel after ICI in more patients, and explored changes in the immune microenvironment. Nineteen consecutive patients with mGEA received ICI followed by ramucirumab/paclitaxel. Most (95%) did not respond to ICI, yet after irRECIST‐defined progression on ICI, all patients experienced tumor size reduction on ramucirumab/paclitaxel. The objective response rate (ORR) and progression‐free survival (PFS) on ramucirumab/paclitaxel after ICI were higher than on the last chemotherapy before ICI in the same group of patients (ORR, 58.8% vs 11.8%; PFS 12.2 vs 3.0 months; respectively). Paired tumor biopsies examined by imaging mass cytometry showed a median 5.5‐fold (range 4‐121) lower frequency of immunosuppressive forkhead box P3+ regulatory T cells with relatively preserved CD8+ T cells, post‐treatment versus pre‐treatment (n = 5 pairs). We then compared the outcomes of these 19 patients with a separate group who received ramucirumab/paclitaxel without preceding ICI (n = 68). Median overall survival on ramucirumab/paclitaxel was longer with (vs without) immediately preceding ICI (14.8 vs 7.4 months) including after multivariate analysis, as was PFS. In our small clinical series, outcomes appeared improved on anti‐VEGFR‐2/paclitaxel treatment when preceded by ICI, in association with alterations in the immune microenvironment. However, further investigation is needed to determine the generalizability of these data. Prospective clinical trials to evaluate sequential treatment with ICI followed by anti‐VEGF(R)/taxane are underway.

Abstract Background Immunosuppression in glioblastoma (GBM) is an obstacle to effective immunotherapy. GBM-derived immunosuppressive monocytes are central to this. Programmed cell death ligand 1 (PD-L1) is an immune checkpoint molecule, expressed by GBM cells and GBM extracellular vesicles (EVs). We sought to determine the role of EV-associated PD-L1 in the formation of immunosuppressive monocytes. Methods Monocytes collected from healthy donors were conditioned with GBM-derived EVs to induce the formation of immunosuppressive monocytes, which were quantified via flow cytometry. Donor-matched T cells were subsequently co-cultured with EV-conditioned monocytes in order to assess effects on T-cell proliferation. PD-L1 constitutive overexpression or short hairpin RNA–mediated knockdown was used to determined the role of altered PD-L1 expression. Results GBM EVs interact with both T cells and monocytes but do not directly inhibit T-cell activation. However, GBM EVs induce immunosuppressive monocytes, including myeloid-derived suppressor cells (MDSCs) and nonclassical monocytes (NCMs). MDSCs and NCMs inhibit T-cell proliferation in vitro and are found within GBM in situ. EV PD-L1 expression induces NCMs but not MDSCs, and does not affect EV-conditioned monocytes T-cell inhibition. Conclusion These findings indicate that GBM EV-mediated immunosuppression occurs through induction of immunosuppressive monocytes rather than direct T-cell inhibition and that, while PD-L1 expression is important for the induction of specific immunosuppressive monocyte populations, immunosuppressive signaling mechanisms through EVs are complex and not limited to PD-L1.

Key Clinical MessageA de novo GFAP variant, p.R376W, was identified in a child presenting with hypotonia, developmental delay, and abnormal brain MRI. Following the 2015 ACMG variant classification guidelines and the functional studies showing protein aggregate formation in vitro, p.R376W should be classified as a pathogenic variant, causative for Alexander disease.